1,166 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
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Tradition and Transformation: Analysis of the Trajectory of Change in Artwork by Artists With an Educational Background in Oriental Painting After Graduation
This qualitative multiple case study tracks the work of six artists with an educational background in oriental painting, traditional Korean painting, for their BFA major. This study reveals a change in their artwork from their original training after graduation to their current manner of artistic expression. This transformation occurs as they develop their artwork in a more contemporary way in the South Korean art world where Western art/global art has become a center. Although oriental painting has been influenced by Western art since Japanese colonial liberation in the 1940s, this situation presents conceptual conflicts between traditional and contemporary approaches to this genre of art. This study examines how six artists find their artistic position between conflicting values through the examination of the trajectory of the changes in their artwork since their graduation from undergraduate school.
The participants of this study were six artists (three men and three women) who earned a BFA degree in oriental painting in South Korea. Semi-structured interviews, visual data of artists’ artworks, and written notes were sources of data analysis. The qualitative case study was based on constructivism of philosophical worldviews. The evolution of the participants’ artwork is examined based on theories such as Jack Mezirow’s transformative learning and Robert Kegan’s adult development.
This study presents the transformation from two perspectives: sociocultural factors and personal motivations. Each perspective reflects changes in materials and techniques as well as changes in imagery. Furthermore, the enduring values of oriental painting in the transformation are examined, which includes Eastern philosophy and aesthetics and visual elements such as three-distance perspective, blank space, and expression of line. Ultimately, this study argues that there exist various avenues of transformation based on oriental painting, with tradition persisting in novel forms of contemporary art
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
Occlusion-Ordered Semantic Instance Segmentation
Conventional semantic ‘instance’ segmentation methods offer a segmentation mask for each object instance in an image along with its semantic class label. These methods excel in distinguishing instances, whether they belong to the same class or different classes, providing valuable information about the scene. However, these methods lack the ability to provide depth-related information, thus unable to capture the 3D geometry of the scene.
One option to derive 3D information about a scene is monocular depth estimation. It predicts the absolute distance from the camera to each pixel in an image. However, monocular depth estimation has limitations. It lacks semantic information about object classes. Furthermore, it is not precise enough to reliably detect instances or establish depth order for known instances.
Even a coarse 3D geometry, such as the relative depth or occlusion order of objects is useful to obtain rich 3D-informed scene analysis. Based on this, we address occlusion-ordered semantic instance segmentation (OOSIS), which augments standard semantic instance segmentation by incorporating a coarse 3D geometry of the scene. By leveraging occlusion as a strong depth cue, OOSIS estimates a partial relative depth ordering of instances based on their occlusion relations. OOSIS produces two outputs: instance masks and their classes, as well as the occlusion ordering of those predicted instances.
Existing works pre-date deep learning and rely on simple visual cues such as the y-coordinate of objects for occlusion ordering. This thesis introduces two deep learning-based approaches for OOSIS. The first approach, following a top-down strategy, determines pairwise occlusion order between instances obtained by a standard instance segmentation method. However, this approach lacks global occlusion ordering consistency, having undesired cyclic orderings. Our second approach is bottom-up. It simultaneously derives instances and their occlusion order by grouping pixels into instances and assigning occlusion order labels. This approach ensures a globally consistent occlusion ordering. As part of this approach, we develop a novel deep model that predicts the boundaries where occlusion occurs plus the orientation of occlusion at the boundary, indicating which side of it occludes the other. The output of this model is utilized to obtain instances and their corresponding ordering by our proposed discrete optimization formulation.
To assess the performance of OOSIS methods, we introduce a novel evaluation metric capable of simultaneously evaluating instance segmentation and occlusion ordering. In addition, we utilize standard metrics for evaluating the quality of instance masks. We also evaluate occlusion ordering consistency, and oriented occlusion boundaries. We conduct evaluations on KINS and COCOA datasets
Examining the Relationships Between Distance Education Students’ Self-Efficacy and Their Achievement
This study aimed to examine the relationships between students’ self-efficacy (SSE) and students’ achievement (SA) in distance education. The instruments were administered to 100 undergraduate students in a distance university who work as migrant workers in Taiwan to gather data, while their SA scores were obtained from the university. The semi-structured interviews for 8 participants consisted of questions that showed the specific conditions of SSE and SA. The findings of this study were reported as follows: There was a significantly positive correlation between targeted SSE (overall scales and general self-efficacy) and SA. Targeted students' self-efficacy effectively predicted their achievement; besides, general self- efficacy had the most significant influence. In the qualitative findings, four themes were extracted for those students with lower self-efficacy but higher achievement—physical and emotional condition, teaching and learning strategy, positive social interaction, and intrinsic motivation. Moreover, three themes were extracted for those students with moderate or higher self-efficacy but lower achievement—more time for leisure (not hard-working), less social interaction, and external excuses. Providing effective learning environments, social interactions, and teaching and learning strategies are suggested in distance education
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